AI 中文总结
针对OCT图像分析的散斑噪声、病变尺度差异及类间相似性问题，本文提出RetiWave-Mamba框架，通过双流设计结合多尺度模块等技术，在OCT-C8数据集上达到98.25%的SOTA分类准确率，可用于临床视网膜疾病诊断。
AI 中文摘要
视网膜疾病是导致不可逆视力损伤的主要原因，早期准确诊断对有效治疗至关重要。光学相干断层扫描（OCT）是实现该目的的关键成像模态，但其自动化分析受固有散斑噪声、病变尺度差异及类间细微相似性的阻碍。为应对这些挑战，本文提出一种名为RetiWave-Mamba的新型框架，将空间-频域学习与最先进的状态空间模型相结合。该框架利用离散小波变换（DWT）将OCT图像分解为低频和高频流，实现结构上下文与细粒度细节的解耦处理。针对低频分支，本文设计多尺度上下文定位模块（MCLM），其将多尺度空洞与空间注意力相结合，以扩展全局感受野并精准定位病变区域；针对高频分支，本文引入配备智能门控机制的注意力引导高分辨率网络（AG-HRNet），以抑制多尺度交互过程中的噪声传播。此外，本文融入频率自适应Mamba投影器（FAMP），以捕获不连续高频纹理特征中的长程依赖关系。在OCT-C8数据集上开展的大量实验表明，本文方法达到98.25%的最先进（SOTA）分类准确率，优于现有方法。这些结果凸显RetiWave-Mamba在噪声条件下稳健识别视网膜病变的效能，为临床诊断提供了极具前景的工具。
英文摘要
Retinal diseases are a leading cause of irreversible vision impairment, making early and accurate diagnosis essential for effective treatment. Optical Coherence Tomography (OCT) serves as a critical imaging modality for this purpose, yet its automated analysis is hindered by inherent speckle noise, varying lesion scales, and subtle inter-class similarities. To address these challenges, we propose a novel framework, RetiWave-Mamba, which integrates spatial-frequency domain learning with state-of-the-art state space models. The framework utilizes Discrete Wavelet Transform (DWT) to decompose OCT images into low- and high-frequency streams, enabling decoupled processing of structural context and fine-grained details. For the low-frequency branch, we design a Multi-scale Contextual Localization Module (MCLM), which synergizes multi-scale dilation with spatial attention to expand the global receptive field and precisely localize lesion regions. For the high-frequency branch, we introduce an Attention-Guided High-Resolution Network (AG-HRNet) equipped with an intelligent gating mechanism to suppress noise propagation during multi-scale interactions. Furthermore, a Feature-Adaptive Mamba Projector (FAMP) is incorporated to form complementary channel-wise feature paths and adaptively reweight them using Mamba-generated gates. Extensive experiments on the OCT-C8 dataset demonstrate that our approach achieves a state-of-the-art (SOTA) classification accuracy of 98.38, surpassing existing methods. These results highlight the effectiveness of RetiWave-Mamba in identifying retinal pathologies and support its potential for computer-aided OCT image analysis.
